Semiconductor manufacturing of integrated circuits is a long, complex, and expensive process. As new demands and technologies are introduced and integrated into semiconductor manufacturing the cost of manufacturing has only increased. A typical semiconductor manufacturing process may require between 200-300 steps. Since equipment malfunctions are inevitable, timely identification of these malfunctions is necessary to maintain profitability of the manufacturing process.
One solution, embraced by semiconductor manufacturers has been a structured approach in which a series of experiments is performed to understand equipment processing conditions. A typical example of this process is illustrated in FIG. 1. FIG. 1 represents a plasma enhanced chemical vapor deposition process. The process outputs of interest are the film thickness of the deposited films, film refractive index, stress on the wafer due to the deposited film, and the film non-uniformity. The process inputs manipulated to get the desired values of the outputs are, three gases G1, G2, G3; radio frequency (RF) power used to create the plasma; and the pressure in the vacuum chamber. Suppose that due to a miscalibration in one of the gas delivery systems the delivered gas flow is different from the requested flow. This could cause one or more of the process outputs to be different from the desired values. Since the future processing steps depend on previous steps, and the functionality of the integrated circuit relies on each set performing to specifications, one would like to quickly identify the miscalibrated gas flow and correct it before it prevents a large amount of semiconductor material from being correctly manufactured.
The diagnosis techniques described in this example make use of process models for fault isolation. Process models describe relationships between process inputs and outputs. Process models can be obtained by two main techniques. The first is by modeling the underlying physics of the process, resulting in physically based models. The second technique ignores the underlying physics but models the process implemented by the equipment as a "black box" by fitting a predetermined functional form to process outputs (responses) at carefully selected inputs. Such models are called response surface models (RSM) discussed in a book entitled Empirical Model-Building and Response Surfaces, published by John Wiley & Sons, New York, 1987. The diagnostic techniques described in this example invention have been tested on RSM models, but could in principle be applied to physically based models also.
This process model illustrated in FIG. 2, directly examines the process output of the equipment but only indirectly examines the operation of the equipment itself by determining the difference between the expected output and the actual output based on a set of measured process inputs. Furthermore this solution often requires sensors to violate internal pressure boundaries of the semiconductor equipment.
Measurement and analysis of vibration data is a well-known method of directly monitoring the operating condition of equipment. Vibration occurs as a normal by-product of the interaction of moving parts within equipment. An individual equipment component may produce a baseline vibration signature. Changes in the equipment component vibration signature indicate a change in the dynamic characteristics of the machine, often caused by a defect or deterioration of moving parts.
The prior art reveals several methods and apparatus for monitoring vibration. Some devices continuously monitor overall vibration in the time or frequency domain, and provide an indication of an alarm condition when preset vibration levels have been exceeded, (e.g., Shima et al., Judging System For Detecting Failure of Machine, U.S. Pat. No. 4,366,544, Dec. 28, 1982).
Another method of vibration analysis is "Vibration Signature Analysis," which is most often accomplished in the frequency domain. Under this method, time-domain vibration data are converted to the frequency domain using a Fourier Transform. The unique frequency spectrum obtained is often termed the "signature" of the machine. A signature of a machine under test may be analyzed and compared to a signature for a normal machine. Differences in the two spectra may indicate an abnormal condition. Prior art devices capable of providing a frequency spectrum are known. One such device includes a handheld probe for collecting vibration data, and the capability of executing a Fast Fourier Transform to provide a frequency spectrum, (e.g., Microlog IMS, available from Palomar Technology International, Carlsbad, Calif.). Morrow also discloses a data acquisition system which performs an automatic frequency spectrum analysis whenever a probable or actual malfunction is detected (Morrow, Data Acquisition System, U.S. Pat. No. 4,184,205, Jan. 15, 1980).
A common problem associated with most of the prior art monitoring equipment is that they usually require a human operator to analyze and compare the signatures.
Prior art inventions lack the sophisticated electronic circuitry and data processing necessary for automatic comparison of the spectra and for rendering a decision regarding the condition of the machine under test, with only minimal human interface.
Prior art inventions are also generally incapable of analyzing machines under transient conditions, and thus find applications restricted to steady state operation. Still other prior art devices are incapable of extracting events, or specific sections of interest in a typical vibration signal. Also, many prior art devices are large and bulky, or require interfacing with a mainframe computer. Finally, most prior art devices require the analysis equipment to be located proximate the machine element to be analyzed. For example, bearings on electric motors are typically monitored by placing sensing devices on the bearing housings themselves.